Blockchain Papers

SK Hafizul Islam, Suchita Malick, Prithwineel Paul

No abstract is available for this record.

Michael C. I. Nwogugu

No abstract is available for this record.

Nghia Dinh, Vinh Truong Hoang, Bay Nguyen Van, Thien Ho Huong · 7 authors

Smart contracts are central to decentralized applications but are often vulnerable to security flaws, both during development and in deployment. Due to their immutable nature, any vulnerabilities introduced are permanent and exploitable, highlighting the need for a secure, structured development lifecycle. This study proposes an adaptive DevSecOps approach tailored to blockchain applications, drawing on established practices and gray literature. By embedding security into Continuous Integration and Continuous Delivery (CI/CD), the model ensures continuous protection from development through deployment. At its core is an adaptive cognitive security framework that automates vulnerability detection using techniques like static analysis, fuzzing, and symbolic execution. It also streamlines secure provisioning across various environments. To further enhance security, Agentic AI is integrated into the pipeline. These autonomous agents monitor code, infrastructure, and behavior in real-time, learning from past incidents, detecting threats, applying patches, and adapting CI/CD workflows dynamically. Experimental results validate the model’s effectiveness, showing significant improvements in the security and reliability of smart contracts. This approach offers a scalable, proactive solution for securing blockchain systems in a rapidly evolving threat landscape.

Pavlo V. Ivakhno, Oleksandr Manoylenko

The article addresses the scientific problem of forming an efficient approach to assessing the market value of business projects in the rapidly evolving area of decentralized finance (DeFi) within the digital economy. The article analyzes the limitations of applying traditional financial evaluation methods, specifically discounted cash flow (DCF) models and economic value added (EVA), which lose their relevance in the DeFi context due to the instability of cash flows, absence of centralized reporting, and the specific profitability structure of tokenized assets. In order to overcome the mentioned limitations in the study, a new multifactor model has been proposed, which combines classical financial indicators with key tokenomic metrics: total value locked (TVL), the utility function of the token in governance, liquidity mining incentives, as well as the distribution of tokens over time (vesting schedules). An empirical validation of the model’s efficiency was conducted through the construction of a multiple linear regression based on data from 20 leading DeFi projects for the years 2023–2024. The results obtained demonstrated a high statistical significance of the included tokenomic variables (p < 0.01) and a high explanatory power of the model (R? = 0.92), confirming its efficiency for predicting the market capitalization of digital assets. It is demonstrated that tokenomic characteristics have a decisive impact on the value of DeFi projects, while traditional indicators (DCF, EVA) are secondary or insignificant due to the changing nature of value in the Web3 economy. The proposed model enables the development of a sound methodology for the strategic analysis of investment attractiveness of decentralized platforms, particularly from the perspective of DAO organizations, venture funds, and analytical agencies.

Yiannis Verginadis, Orestis Almpanoudis, Dimitris Apostolou, Marcela Tuler de Oliveira · 5 authors

Enterprise Information Systems have a long-established and crucial role for modern organizations, as they enable seamless integration and management of critical business processes, ensuring efficiency in operations, data accuracy, and enhanced decision-making capabilities. One of their most interesting emerging technologies refer to the use of Artificial Intelligence as they may seamlessly automate routine tasks, offer predictive analytics, and provide deep insights, ultimately leading to intelligent data-driven decisions and improved operational efficiency. Of course, this direction of work is accompanied by some important challenges that come from the opacity of certain AI models and their potential biases due to low-quality training data used. In this paper, we argue that such challenges can be mitigated by a novel framework able to integrate, in a transparent manner, quality-related metadata on datasets used for training the AI-enabled emerging technologies in the field of EIS systems. These metadata are minted as Non-Fungible Tokens (NFTs) over the blockchain.

Ashley Chenn

No abstract is available for this record.

Olamide Emmanuel Ayodele, Temiloluwa Iyanuoluwa Ajibade, Oluwadare Joshua Oyebode, Kayode Akinlekan Akintoye · 9 authors

No abstract is available for this record.

Seyed Mojtaba Hosseini Bamakan, Farnaz Dehghan, Saeed Banaeian Far, Ahad Zareravasan

Cyber-Physical-Social Systems (CPSS), as emerging paradigms, are evolving to address the growing need for intelligent, adaptive, and transparent decision-making in complex environments such as smart cities and industrial systems. However, the advancements enabled by Digital Twins (DTs), centralized governance models, and opaque analytics limit scalability and resilience. In this study, we propose a decentralized governance framework that integrates Decentralized Autonomous Organizations (DAOs) and blockchain-based predictive analytics to enhance trust, interoperability, and ethical decision-making for CPSS. The proposed framework utilizes immutable ledgers, automated smart contracts, and community-driven governance to improve real-time collaboration, thereby ensuring system resilience and facilitating adaptive optimization. The remarkable innovation of the CPSS framework lies in combining digital tokens (DTs) within a blockchain application. We believe that our approach provides a scalable mechanism for autonomous decision-making and secure data sharing across multi-stakeholder ecosystems. In this regard, using theoretical analysis and comparative evaluations, we have demonstrated how our framework mitigates conventional challenges in CPSS governance, including security vulnerabilities, algorithmic fairness, and data integrity. Moreover, this research makes a significant contribution to the advancement of decentralized digital twin (DT) infrastructures, paving the way for more robust, ethically aligned, and resilient cyber-physical systems.

Fabian Gast, Viktor Berchtenbreiter, Felix Hoffmann, Matthias Weigold

No abstract is available for this record.

Feida Zhu, Bingxue Zhang

No abstract is available for this record.

R. Ramı́rez, Raja R. A. Issa

No abstract is available for this record.

Igor Calzada, Géza Németh, Mohammed Salah Al-Radhi

As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe lighthouse project called Enfield: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) Zero-Knowledge Proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) Privacy-Preserving Machine Learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization.

Qian Wang, Zhuo Wang

In engineering monitoring and the Internet of Things (IoT) sensor networks, ensuring data security and real-time performance is a critical challenge. Traditional centralized data management faces issues such as high risks of data tampering, insufficient privacy protection, and inconsistent data sharing among multiple parties, making it difficult to handle high-frequency updates and complex collaboration. To address these issues, this article proposes a blockchain-based improved consensus algorithm, Reputation-based Practical Byzantine Fault Tolerance (RPBFT), to enhance the management efficiency and security of IoT sensor data. RPBFT enables secure data storage and sharing through smart contracts and introduces a node behavior incentive mechanism and a simplified consensus protocol to reduce communication overhead and consensus latency. Experimental results show that RPBFT reduces consensus latency by approximately 30%, increases throughput by 70%, significantly reduces energy consumption, and exhibits strong resistance to attacks. The study demonstrates that RPBFT is suitable for resource-constrained IoT sensor networks and can enhance performance and security in larger-scale and more complex network environments. Furthermore, the proposed algorithm provides practical implications for improving data reliability and system efficiency in real-world engineering monitoring scenarios, such as structural health monitoring and environmental safety supervision.

Abdul Jabbar, Pervaiz Akhtar, Syed Imran Ali

Abstract This study explores the interplay between blockchain-based smart contracts and big data analytics for the supply chain value creation of micro, small, and medium enterprises (MSMEs). We implement our Ethereum Virtual Machine (EVM) procedure with the ganache blockchain, and addresses generated by the Metamask wallet. Each supply chain player in the blockchain is assigned a wallet address to observe the hashes created when data is added to the blockchain. Our findings unfold that supply chain value creation emphasises traceability, transparency, security, and profit maximisation interlocked with how effectively companies utilise big data collected through blockchain-based smart contracts. This subsequentially assists managers in using data types and a variety of analytics, spanning from descriptive, diagnostic, predictive, and prescriptive to cognitive analytics. This synergy between the blockchain and the types of analytics provides opportunities to identify new interactions and directions for future research.

Siti Hajar Mohd Yusof, R. Zahilah, Siti Hajar Othman

This research explores the development of a hybrid consensus algorithm that combines the benefits of Proof of Authority (PoA), Delegated Proof of Stake (DPoS), and threshold cryptography to create a secure, efficient, and scalable consensus mechanism for resource-constrained devices. The proposed algorithm addresses traditional consensus algorithms' limitations in resource-constrained environments, where energy efficiency, security, and decentralisation are crucial. By leveraging the strengths of PoA, DPoS, and threshold cryptography, this hybrid approach is anticipated to provide a robust and adaptable consensus mechanism to support many applications in IoT, edge computing, and other resource-constrained domains. The research aims to investigate the feasibility, performance, and security of this hybrid consensus algorithm and its potential to enable secure, decentralised, and scalable blockchain-based systems for resource-constrained devices.

Ajibola Oluwafemi Oyeleye, Onyeka Franca Asuzu, Adaobi Vivian Ibeh

This paper presents a conceptual model for raising Accounts Payable (AP) accuracy in research institutions by embedding process intelligence across the procure-to-pay lifecycle. The model integrates process mining, rule-based controls, and machine-learning anomaly detection with grant compliance logic to reduce mismatches, duplicate payments, and breaches. It addresses the context of universities and research hospitals, where varied funding sources, sponsor terms, and decentralized purchasing create transaction patterns and compliance risk. The model positions AP as a data-driven assurance hub connecting principal investigators, central finance, and suppliers. The architecture has four layers: first, data acquisition that unifies ERP, e-procurement, and grant management logs via standardized event schemas; second, conformance engines encoding sponsor allowability, period of performance, three-way match, and delegation rules; third, analytics and prediction that combine process discovery, first-pass-yield forecasting, vendor normalization, and exception clustering; and fourth, workflow orchestration that returns prescriptive alerts to case managers and routes exceptions to approvers for timely resolution. Methodologically, the model adopts a design-science and DMAIC hybrid. Teams baseline cycle time, touchpoints, and first-pass accuracy; mine event logs to map as-is variants; prioritize failure modes through FMEA; implement targeted controls; and measure effects with interrupted time series and segmented regression. Data quality is elevated through master-data maintenance, vendor deduplication, and invoice OCR confidence thresholds with human-in-the-loop review. Expected outcomes include higher first-pass yield, fewer late-payment penalties, improved sponsor billing, and cleaner audit trails. Leading indicators exception rate, conformance score, and rework loops feed a control chart to sustain gains, while lagging indicators write-offs, questioned costs, and audit findings confirm risk reduction. The model also incorporates equity and accessibility by simplifying small-supplier onboarding and enabling transparent status notifications to reduce inquiry volume and payment anxiety. A change-management plan aligns incentives across finance, research administration, and procurement, with skills uplift delivered through training and playbooks. This conceptualization offers a scalable blueprint aligning AP accuracy with research integrity, stewardship of public funds, and overall operational resilience, enabling institutions to realize predictable, compliant payables operations and stronger supplier relationships.

Somnath Mondal, Sujan Das, Shib Shankar Golder, Rajesh Bose · 6 authors

As Artificial Intelligence (AI) and Big Data continue to evolve at a rapid pace, personalised medicine in healthcare has undergone revolutionary changes. Despite these advancements, current AI-driven systems encounter substantial hurdles in security, scalability, and privacy, particularly when handling sensitive patient data across decentralised networks. This paper introduces an innovative AI-Driven Big Data Analytics Framework that incorporates Federated Learning, Blockchain technology, and Quantum Computing to enhance personalised medicine. The proposed architecture ensures robust data privacy, real-time analytics, and secure patient data sharing, leading to significant improvements in healthcare diagnostics, treatment planning, and prognosis prediction.

Xiufeng Li, Lei Li, Shaojun Ma

In recent years, various digital Business-to-Business (B2B) platforms have been accelerating the promotion of digital transformation in manufacturing. Consider a supply chain setting where an online B2B platform offers Internet of Things (IoT) service and selling channels to a manufacturer, this paper examines digital innovation investments and service pricing decisions under two common contracts: sell-on and sell-to contracts. Firstly, we have identified the impact of demand spillover from IoT platform services and IoT technology on manufacturer innovation and product line decisions. We found that under significant demand spillover , the manufacturer will exclusively produce smart products and discontinue the production of traditional products. Secondly, we find that under sell-on contracts, the innovation investments of the platform and manufacturer are always substitutable as the platform commission rate increases. Also, both the manufacturer and the IoT platform tend to increase innovation provision when using sell-on contracts compared to sell-to contracts. Finally, our results illustrate that the profitability of the IoT platform and the manufacturer is contingent upon the type of contract in place, with the IoT platform being more profitable under sell-to contracts when demand spillovers are small. Our research findings offer a valuable reference point for developing IoT platforms and insights into innovation and pricing decisions for smart device manufacturers in the digital transformation of manufacturing.

Adwaita Raj Modak, K. Niha, P. Swarnalatha, Gandhi Kishor Addanki

Blockchain technology introduces decentralized and immutable ledgers that provide trust and transparency in data management whereas smart contracts can automate business processes. Accessing, and querying data stored on blockchain networks can be challenging due to their complex data structures and limited querying capabilities. In this chapter, we will enhance smart contract-enabled chains' functionality by developing a decentralized database and distributed indexer system capable of executing SQL-like queries on blockchain data. GunDB, a decentralized database known for its robustness and flexibility, enables efficient and simplified data retrieval from blockchain networks. By integrating this database with smart contract-enabled chains, we bridge the gap between blockchain technology and traditional querying methodologies, enabling developers and users to access blockchain data using Structure Query Language-like syntax. Through a carefully designed indexing process, we ensure that the data is efficiently indexed and retrieved with security and immutability characteristics.

Tiago Guimarães, Ricardo Duarte, Francini Hak, Manuel Filipe Santos

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed.

Mahmoud Allahham, Atheer Ahmed Alrashed, Omaia Al-Omari, Wasef Ibrahim Almajali

This justifies the feature of blockchain in decision-making where companies predominantly use data processing and managing methods or have intricate supply chains demanding authenticity and relying on blockchain technology. This study is motivated by increasing convolutions and demands for secure speedy supply chain system as it is important in nurturing trustworthiness and accountability during commercial transactions, so it focuses on showing why current supply chain authentication methods are inefficient and unreliable leading to potential frauds and mistrust. This research starts by reviewing existing supply chain authentication methods and their shortcomings. The Basics of Blockchain refer to How Decentralized and Secure it really is, explaining what blockchain technology is. This blog post discusses how blockchain can be used to enhance decision-making processes, providing an unchangeable, auditable way to keep track of and authenticate the supply chain, and briefly covers various use cases of blockchain in supply chains and explains how they are transforming the security and efficiency of operations. The objective of this study is to suggest a structure that combines blockchain and machine learning for better supply chain decisions and authentication systems. It makes use of decentralized system architecture from methodological point of view by which the immutability aspect of blockchain and machine learning analytics can be integrated together in forecasting of demand while at the same time ensuring safety for transactions. Overall, blockchain technology enables significant disruption in supply chain authentication by helping partner systems and organizations make better decisions and reinforcing security requirements between partners. Combined with blockchain, RFIDs provide improved transparency and cost savings and ensure data is secure and trustworthy, thereby greatly enhancing supply chain networks. Indeed, the findings have shown clearly that coherent use of these technologies in supply chain management yields tangible improvements in such areas as fraud prevention, operational efficiency and even customer trust.

Emmanuel Syrmoudis, Stefan Mager, Jens Großklags

Lock-in practices of online services hinder consumers from switching frictionlessly to a competitor once they are unsatisfied with the company’s service offering, privacy practices, or philosophy. The right to data portability (RtDP) is one of the strongest measures introduced by recent privacy regulations to unlock continuously collected user data from centralized silos of market leaders. Introducing the obligation to provide means of data transfers between services, it aims to establish decentralized online markets and to foster competition. In this longitudinal study comprising a unique dataset of 129 online services over three consecutive years, we are the first to provide evidence on the development of the effectiveness of the EU’s RtDP. Astonishingly, only 16% of services could provide a compliant data export in all years, with services from the industries Entertainment and Travel performing worst. Overall, Finance & Insurance and Social Networks & Messaging include the services with the highest compliance rates. Regarding the usefulness of data portability, our analysis unveils that data export scope and data import options have stagnated between 2020 and 2022. Further, we are able to show that online services with a high presence of third-party trackers are less compliant and ready to export data from their systems. Lastly, our regression analyses show that service popularity significantly increases format compliance, export scope, and import options. This suggests that competitors to incumbents still perceive the regulation more as a bureaucratic burden than a unique opportunity to attract new consumers and their data.

Junaid Nasir Qureshi, Muhammad Shoaib Farooq, Usman Ali, Adel Khelifi · 5 authors

Distributed DevOps is a software development methodology that aims to integrate the work of development and operations teams without being bound by geographical constraints. This methodology excels in enhancing collaboration and speeding software development. However, it does suffer from a lack of security, transparency, and traceability, which can result in project delays, a lack of trust between stakeholders, and even project failure. This paper addresses these issues of Distributed DevOps by implementing Blockchain technology. In this paper, we propose a novel framework that leverages blockchain technology to address the challenges faced by Distributed DevOps. Through performance analysis, we demonstrate the effectiveness of our framework in a real-world scenario, highlighting its ability to improve transparency, traceability, and the security of the DevOps pipeline. Our findings underscore the potential of blockchain-empowered solutions in revolutionizing DevOps practices. Furthermore, this research offers a practical framework for organizations seeking to optimize their development processes by integrating blockchain technology.

Simon Curty, Hans-Georg Fill

No abstract is available for this record.